Method and system for targeted content placement

ABSTRACT

A system for delivering targeted content to a device includes a database and a processor. The database stores anonymous identifications for a plurality of customers of a financial institution. Each anonymous identification is associated with financial information for a respective customer of the financial institution. The processor includes software that is configured to classify consumers, create a targeted list of content viewers, and deliver a targeted advertisement over a network to a device used by a content viewer on the targeted list. The classifications can be based on the financial information associated with each anonymous identification. The target list of content viewers can be based on at least one selected classification. The advertisement can be delivered to a device used by a content viewer on the targeted list after the device has been used to log onto an application offered by the at least one financial institution.

This application is a continuation of U.S. patent application Ser. No.12/266,199 filed Nov. 6, 2008 (METHOD AND SYSTEM FOR TARGETED CONTENTPLACEMENT) which is incorporated herein by reference in its entirety.This application claims priority to U.S. Patent Application 61/037,020filed Mar. 17, 2008 (METHOD AND SYSTEM FOR TARGETED INTERNETADVERTISEMENT PLACEMENT) which is incorporated herein by reference inits entirety. U.S. patent application Ser. No. 11/865,466 filed Oct. 1,2007 (PERSONALIZED CONSUMER ADVERTISING PLACEMENT) is also incorporatedby reference herein.

BACKGROUND

1. Field

The following description relates generally to the targeting of mediacontent to consumers. More particularly, the embodiments relate to dataarchitecture and processing, and a database solution that willultimately deliver personalized content, including advertisements,video, audio, audio/visual, text-based content, or any personalizedmedia to a device, where the device can store a delivered identificationand is operated by a consumer who is classified in a classificationderived from financial information for that consumer.

2. Description of Related Art

Increasing Internet usage where nearly one billion global users accessthe Internet on at least a monthly basis is reflected by greatercorporate spending on Internet advertising and more compelling andeffective marketing technologies and practices. For example, Standardand Poor states that keyword search revenues contributed notably tothese gains, with 182% growth in 2003, 51% growth in 2004, and 34%growth in 2005, largely reflecting the successes of Google and Yahoo.These companies garner revenues from specific user on-line queries thatgenerate sponsored Internet links and associated click-throughs. Thispattern of growth in Internet advertising is anticipated to continuewell into the future.

To make their advertising dollars more effective, advertisers attempt totarget their advertising to individuals who are more likely to have aninterest in the advertised product, thereby producing a higherclick-through rate and increased revenues. Of course, in order to targetindividuals with any degree of accuracy, something must be known aboutthe individual. For this reason, technologies have been developed forwhat is known in the art as behavioral targeting based on tracking auser's habits through monitoring of the websites that the user visits,and offering targeted advertising based on the content of the visitedwebsites. It is assumed, for example, that if a user is visitingautomobile oriented websites, then an automobile oriented advertisementis more likely to generate a user response than one for breakfastcereal. A problem with this type of website tracking is that if anautomobile advertisement for a very expensive car is delivered to a userand he cannot afford to purchase the automobile, then this advertisementis not very effective.

While the above-described behavioral profiling has been somewhateffective in improving the effectiveness of Internet advertising, suchbehavioral profiling is unable to accumulate data related to anindividual's particular spending habits. For this reason, some marketershave developed methods to retain customers who have initiated purchasesfrom them by tracking their purchasing habits and trends. However, thistracking of purchases is limited to knowledge of purchases placed on themarketer's own websites. While a marketer such as, e.g., Amazon mighttrack a consumer's shopping habits so that when the consumer logs intoan Amazon website at a future time, advertisements can be automaticallyplaced showing suggested items for the consumer to consider based ontheir previous purchasing habits. Of course, Amazon would have noknowledge of the customer's purchasing habits at retailers notaffiliated with Amazon. Therefore, the tracking methodologies used byindividual on-line retailers are limited in the benefit that they canprovide to the retailer.

SUMMARY

A system for delivering targeted content to a device includes a databaseand a processor. The database stores anonymous identifications for aplurality of customers of at least one financial institution. Eachanonymous identification is associated with financial information for arespective customer of the at least one financial institution. Theprocessor includes software that is configured to classify consumers,create a targeted list of content viewers, and deliver a targetedadvertisement over a network to a device used by a content viewer on thetargeted list. The classifications can be based on the financialinformation associated with each anonymous identification. The targetlist of content viewers can be based on at least one selectedclassification. The advertisement can be delivered to a device used by acontent viewer on the targeted list after the device has been used tolog onto an application offered by the at least one financialinstitution.

A method for delivering targeted content to a device can includereceiving financial information for a plurality of consumers, generatingclassifications for the plurality of consumers, selecting at least oneof the classifications for targeted content, creating a list of targetedconsumers selected from the plurality of consumers based on the selectedclassifications, and delivering the targeted content to the targetedconsumers on the created list. The financial information can be receivedby a targeted content delivery system from at least one financialinstitution. The consumer financial information can be associated with aunique anonymous identification for each of the plurality of consumers.The classifications can be based on the received consumer financialinformation.

The foregoing summary is provided only by way of introduction. Allfeatures, benefits, and advantages of the personalized consumer contentarchitecture/solution may be realized and obtained by instrumentalitiesand combinations particularly pointed out in the claims. Nothing in thissection should be taken as a limitation on the claims, which define thescope of the invention.

The subject development is also applicable to the other entities orfinancial institutions who maintain personalized web sites inassociation with customers' financial data, such as insurers, investmentcounselors, brokers or the like.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a block diagram depicting processes for generating lifestyleindicators for consumers.

FIG. 2 is a block diagram depicting processes for matching receipt-levelretail transactions to account-level bank transactions.

FIG. 3 is a block diagram depicting processes for creating and updatingan advertising campaign based on the classifications.

FIG. 4 is a flow diagram depicting a method for generating lifestyleindicators for consumers.

FIG. 5 is a flow diagram depicting a method for matching receipt-levelretail transactions to account-level bank transactions.

FIG. 6 is a flow diagram depicting a method for creating and updating anadvertising campaign based on the classifications.

FIG. 7 is a block diagram depicting a system for generatingclassifications for consumers, and for creating and updating anadvertising campaign based on the classifications.

FIG. 8 is a block diagram depicting a method for generating apersonalized advertisement.

FIG. 9 is a flow diagram depicting a method for delivering targetedcontent to a networked device.

FIG. 10 is a flow diagram depicting a method for recording a hashed IPaddress.

FIG. 11 is a flow diagram depicting another method for deliveringtargeted content to a networked device.

DESCRIPTION OF THE EMBODIMENTS

The following is a description of a system and method of deliveringpersonalized content, e.g. advertisements, video, audio, audio/visualand text-based content, for an anonymous network user on a network suchas, e.g., the Internet. An example of such a system is described so thatone may construct the system, however, the embodiments which are definedby the appended claims are not limited to the system described herein.

With reference to FIG. 1, a diagram is provided in block form forshowing the flow of information in a system for determiningclassifications for individuals who will be receiving targeted contentover a network, e.g., the Internet. The diagram is provided for thepurpose of explaining interrelationships between various data in thesystem, and the embodiments of the present application are not limitedto the arrangement shown. Although each of the blocks in the diagram isdescribed sequentially in a logical order, it is not to be assumed thatthe system processes the described information in any particular orderor arrangement.

Before describing each of the processes shown in the figure in detail, abrief explanation of terms and object of the processing is provided.Four types of information are described in FIG. 1. Bank consumerinformation 14 is anonymous demographic data about a consumer as anindividual such as, e.g. birth date, gender, zip code, etc. The bankconsumer information utilized in preferred embodiments is data that doesnot reveal the identity of the consumer. Accordingly, the bank consumerinformation used in the embodiment described herein does not includename, street address, social security number (or any other governmentissued identification number), e-mail address or telephone number. Bankaccount type information 13, on the other hand, while also beingassociated with the consumer, provides information related to an accountor accounts held by the consumer at the bank or financial institution.The account type information includes data describing the type ofaccount(s), e.g., savings, checking, mortgage, IRA, credit card account,asset loan etc. It also includes information related to each account, asapplicable, such as inception date, terms for mortgages or certificatesof deposit, payment amounts for mortgages or other loans, and so forth.Similar to the bank consumer information, in the embodiment describedherein the bank account type information does not include the accountnumber of the consumer. This further protects the privacy of theconsumer by anonymously identifying the consumer in a manner that willbe described in more detail herein. Bank transaction data 10 describesindividual transactions transacted by the consumer such as, e.g. acredit/debit card purchase at a retailer location. The bank transactiondata includes information such as a merchant/retailer identifier, thetotal transaction dollar amount of the transaction, the type oftransaction, the location of the merchant, etc. Bank transaction dataalso includes automated clearing house (ACH) transactions and automaticwithdrawals. The bank transaction data 10 typically does not, however,provide detailed line item information for each item that was part of amultiple-item purchase. In preferred embodiments, however, retailers orservice providers opt to provide receipt-level retail transactioninformation 32. This information is more detailed and includes line itemdetail identifying each item and quantity purchased from theretailer/service provider, along with the price of the individual items.Although not essential to determining classifications, described morefully below, the system and processes described herein can protect theidentity of the consumer by anonymously identifying the consumer basedon the data described above. This can protect the consumer from securitybreaches, where the consumer's identity can be used to his detriment,e.g. using a consumer's identity for fraudulent transactions.

The use of each of the above-described types of information is describedin more detail below. However, an object of embodiments of the presentapplication is to classify consumers into lifestyles or classificationsbased on the above-described information. These classified lifestylesare provided so that targeted content can be delivered to the consumersbased on the lifestyle of the particular consumer. The classificationscan also protect a consumer's identity by referring to a consumer as amember of a class as opposed to as a specific individual. Theclassifications can be deduced based on the status of the consumer,e.g., whether the consumer is a grandparent, a college student, a singlemother, etc. The classification can be based on the activities in whichthe consumer participates such as golfing or attending theatres. Theclassification can also be based on what the consumer owns, e.g., a homeowner or a boat owner, or on the income of the consumer. Theclassification can include multiple classifications, e.g. a grandparentwho golfs. The classification is meant to classify consumers intocategories helpful to merchandisers seeking to deliver targetedadvertisements. The classifications can also help publishers to providemore meaningful content to viewers of the content. Examples of suchclassifications include, but are not limited to grandparent, collegestudent, self employed, theatre/movie attendee, investor, parent,traveler, region of residence or travel, etc. The classificationsdescribed herein are hierarchical in nature. For example, if a consumeris identified and classified with a classification as a “mother”, theclassification for mother falls under a higher-level classification for“female”. This hierarchical relationship is discussed in further detailherein.

First described, are incoming bank transactions. Although the term “banktransaction” is used throughout this application, it is to beappreciated that the transaction may be related to any form of financialinstitution with which a user of the system conducts business or has arelationship. Typically, the financial institution will provide on-lineservices for the user enabling the user by means of a unique user ID andpassword, or other security data, to either log in or sign in with awebsite or other networked portal or application affiliated thefinancial institution for conducting business. When the user logs on tothe financial institution's website or other networked application, theuser is identified by a unique customer identification code (UCIC)assigned by the financial institution. The UCIC does not include thefinancial institution's customer password or unique user ID used to logonto the financial institution's website, nor does it include thecustomer's account number, name, street address, social security number,e-mail address or telephone number. The UCIC is matched to an anonymousidentifier, hereinafter referred to as an advertisement deliveryidentification code (ADIC), which anonymously but uniquely identifiesthe user. Even though the term ADIC is used to refer to theidentification, this identification can be useful when delivering othertargeted content, e.g. video, text-based content that does not market aproduct. The ADIC is assigned by the entity that will deliver thetargeted content or will associate with another publisher to delivermore relevant content. Both the ADIC and the UCIC are described in moredetail in the above-referenced application Ser. No. 11/865,466. The ADICcan also be specific to the device the financial institution's customeruses to access the financial institution's website or other networkedapplication. For example, if the one customer use a lap top computer anda mobile phone to access the financial institution's website or othernetworked application, this same customer can be associated with twodifferent ADICs, one for each device.

Three types of information received from financial institutions arediscussed herein, i.e., bank consumer information, account typeinformation, and account-level bank transaction information. Aspreviously described, each received bank transaction includesinformation relating to a particular bank transaction, and the bankconsumer information includes account-level detail informationpertaining to the particular consumer. For example, the system receivesconsumer level information 14 that may include birthday and zip codewhich are useful so that, e.g., age and location can be determined forthe consumer. Account type information 13 provides additionalinformation related to the consumer such as the types of accounts heldat the financial institution. The account-level bank transaction 10, onthe other hand, provides information related to a purchase of goods orservices from a merchant. It is to be understood, however, in preferredembodiments, the received information does not reveal the identity ofthe consumer, e.g., name, street address, social security number,telephone number, account number, credit card number, e-mail address,etc.

The account-level bank transaction data 16 includes, e.g., merchant SICcode, merchant description, price, asset information if the transactionrelates to a loan, location of the merchant, preferably a zip code forthe merchant, and the type of transaction. The standard industrialclassification (SIC) is a code developed to classify establishments bythe type of activity in which they are primarily engaged. For example,the SIC code 2043 represents cereal breakfast foods, and SIC code 4521represents department stores. It is to be appreciated that theaccount-level bank transaction data 16 described herein is exemplaryonly. The data contained in the account-level bank transaction recordcan vary according to both embodiment and according to the financialinstitution providing the bank transaction 10. For example, the SIC codeis being replaced with the North Atlantic Industry Classification System(NAICS). The NAICS code serves substantially the same purpose as the SICcode, however, the NAICS code has been extended to six digits incontrast to the four digit SIC code in order to accommodate a largernumber of sectors and allow more flexibility in designating subsectors.The merchant description is usually a string of characters, which can beutilized in on-line banking, describing the particular merchant. Theasset code, utilized with loans, can be useful for providing additionalinformation. For example, each time a car payment is made, the systemcan determine what kind of car the consumer drives based on theinformation provided, e.g., the asset for which the loan was made.

The information provided in the account-level bank transaction data 16and the bank consumer information data 14 can be useful in classifyingcustomers and in developing the classifications 26. For example, thebank consumer information 14 provides a zip code related to theconsumer, and the account-level bank transaction data 16 can provide azip code related to the merchant. Based on these two zip codes, onanalyzing purchases made for gasoline, the system is able to deduce whatthe consumer's approximate daily commute is, and how much the consumeris spending on gasoline. With this information, the consumer can beclassified into a classification “commuter between 15-25 miles.”Similarly, other classifications can be deduced from the transactiondata. Another example of a classification is the user's debt/equityratio, which represents the amount of debt for the user versus theamount of cash savings at the bank, or in the case of assets, someassets have an appraised value such as a car, home, etc. The debt/equityratio can be deduced based on bank transaction data 16 and can be usefulin targeting advertising to a firm or advertiser seeking investors.

As the bank consumer information 14 and the account-level banktransaction data 16 are being received, the information data is funneledthrough a taxonomy classification system 18. The taxonomy classificationsystem 18 is where the system starts developing classification data. Forexample, based on analysis of the consumer information and account-leveltransaction information, the system can determine if the consumer is anSUV driver (based on the asset for which a loan has been secured), ahomeowner (based on mortgage information), etc. The taxonomy systemclassifies consumers according to a classification system similar to thepreviously described SIC codes and NAICS codes or any industry supportedclassification. The SIC and NAICS codes however, are typicallyinadequate for purposes of embodiments of the present application and,therefore, a taxonomy classification coding that can go much deeper andprovide more detailed classifications is utilized. Some suitabletaxonomy classifications are known in the art, and it is to beappreciated that any arbitrary taxonomy classification coding or systemcan be utilized by the taxonomy classification system 18. Developing ataxonomy classification system, however, is an intensive andtime-consuming process. Therefore, embodiments of the presentapplication may utilize existing taxonomy classifications when possible.

An example here is useful in describing how the taxonomy classificationsystem is able to deduce classifications based on the incoming bankconsumer information and account-level bank transaction data. Forexample, an account-level bank transaction is received including an SICcode for a debit card purchase. As an example, the consumer made apurchase (or more likely repeated purchases) at Starbucks, and based onthe SIC code representing coffee retailer, the consumer may beclassified as a coffee drinker. As another example, a consumer may beassociated with an asset code via the account type information 13 withan SUV (the consumer has a car loan for an SUV). Based on this, theconsumer may be classified as an SUV driver. A taxonomy classificationcode is thereby assigned accordingly. It is to be appreciated also thatno single taxonomy classification system can cover every possibleclassification and, therefore, taxonomy classification systems aresubject to growth and evolution over time. The taxonomy classificationsystem 18 analyzes incoming bank consumer information and account-levelbank transaction data to deduce classification codes that are notprovided in the incoming data. For example, the incoming consumer andaccount-level data does not typically include whether a consumer is agrandparent or an SUV driver. These classification codes can however bededuced by analysis and correlation of the incoming transactions andconsumer information 14 for a given consumer.

It is to be appreciated that, although the merchant SIC code identifiesonly the merchant rather than the specific merchandise being purchased,arrangements can be made with specific merchants to obtain receipt-leveldata. In this case, the account-level transaction record information 10can be combined with receipt-level retail transaction data 32 in amatching process 34 to identify specific products purchased at themerchant location. Combining this information can advantageously enablepredicting future purchases based on the prior history of purchases. Forexample, when the account-level transaction record identifies that theuser has shopped at a hardware store, and the receipt-level dataindicates that the user has recently purchased drywall, drywall screws,and drywall compound, the system can automatically deduce that one ofthe consumer's next likely purchases will include sandpaper and/orpaint. Those are referred to herein as collateral purchases and are ofparticular interest to advertisers because of the high likelihood ofinterest from the consumer in advertisements that are correctly targetedtoward their next likely purchases.

The account-level bank transactions 10, the bank consumer information14, the bank account type information 13 and the retail transaction data32 can be further processed in an aggregate assumption phase 20. In theaggregate assumption phase, multiple account-level bank transactions fora particular consumer can be aggregated to make reasonable assumptionsabout the consumer's spending habits. Further, assumptions can bederived regarding annual income, when a raise is received, and whenannual bonuses might be received. Assumptions regarding annual incomecan be derived based on recurring bank deposits and so forth. Automobileloan or lease transactions can be aggregated to determine the length ofan auto loan or lease for the consumer. This sort of information can beparticularly useful for targeting advertisements to the consumer as theyapproach the end of a loan or lease period. Contract data, such as,e.g., wireless contracts, insurance contracts, and utility contracts canbe determined based on aggregation of the account-level banktransactions and retailer receipt-level data. Additional usefulinformation which can be aggregated includes, but is not limited to,annual travel habits based on change of merchant zip codes, what citiesare traveled, what time periods such travel is likely to occur, and soforth.

Assumptions deduced by the aggregate assumption phase 20 can be usefulin generating classifications 26 and classifying consumers intoclassifications. For example, suppose that an advertiser wants to targetindividuals who travel at least 100,000 miles per year. Aggregateassumptions about annual travel from the aggregate assumption phase 20can, based on trigger criteria, be utilized in a “Bubble-Up” process 22to classify particular consumers' as travelers of at least 100,000 milesper year. Similarly, persons belonging to a grandparent taxonomyclassification can be identified based on birth dates and data from theaggregate assumption phase 20 by examining purchases, over time, fromtoy stores. A person buying toys within the approximate same range ofmoney each time every year at toy stores, particularly if the person ispast age 60 for example, can be a good indication that the consumer is agrandparent. Further, this aggregate information can be useful inestimating grandchildren birthdays. And even further, based on the toysbeing purchased and the purchasing patterns, an estimation of thegrandchildren's age can be determined. This information Bubbling Up tothe classifications 26 can be very useful to advertisers for targetingadvertisements to these consumers at appropriate times during the year.

It is to be appreciated that the Bubble-Up process 22 is not necessarilya continuous process that occurs with each received account-level banktransaction, but rather, triggers can be established in the aggregateassumption phase 20 such that when enough information has beenaggregated to make the assumption useful as a classification, theinformation can then be Bubbled Up to the classifications 26. Referringagain to the hierarchical nature of the classifications, informationBubbling Up as a classification can continue from the lowest leveltouched by the information to higher levels in the classification,touching elements on the way up. For example, a consumer identified as amother can affects higher levels, i.e., female at the top level.However, the classification structure can be further described as beingpoly-hierarchical, i.e., a fork occurs when traversing theclassifications in an upward direction. As an example, a consumeridentified as a mother with a teenager, with aggregated transactionsincluding six transactions in the last 12 months over $100, and having adaughter can affect multiple higher levels.

The taxonomy classification codes developed in the taxonomyclassification phase 18 and/or transaction information are furtherprocessed in a complement identification phase 24. The complementidentification phase 24 applies analytics for determiningclassifications 26 related to collateral transactions. For example, foodpurchases may be indicative of someone likely to view movies. A new carpurchase could be an indication that car washes will be purchased morefrequently. A grandparent, would likely purchase toys on a grandchild'sbirthday. Similarly a trend identification phase 23 applies analyticsfor determining classifications 26 related to identified purchasingtrends. These identified trends are not necessarily obvious and are notdetermined based on obviousness, but instead are determined based onobserved trends. For example, the system may determine over time thatpurchasers of iPhone mobile phones are likely to purchase jeans. Theseidentified trends also contribute to the development of classifications.As with the aggregate assumption process 20, the complementidentification phase 24 and the trend identification phase 23 alsoutilize the Bubble-Up process 22 to Bubble-Up information into theclassifications 26.

The classifications represent information about a consumer related totheir lifestyle which is useful to advertisers for targetingadvertisements. The classifications also represent information about aconsumer that is useful to website publishers. Examples ofclassifications include identification as a grandparent, collegestudent, self-employed person, theater/movie attendee/watcher, investor,parent, and traveler. These examples, however, represent just a samplingof possible classifications used in embodiments of the presentapplication. It is to be appreciated that, similar to the evolution oftaxonomy classification codes, new classifications can be added overtime and evolve as the need arises. Some classifications may expire orbe removed over time, e.g., “married” becomes “divorced”, which can alsobe “single,” reinforcing the poly-hierarchal nature of theclassifications.

The classification of a consumer can also be weighted for trueness. Forexample, for the classification “grandparent,” a consumer is either agrandparent or not a grandparent—it is a true or false condition. Sincethe system does not know the identity of the consumer, and it isdesirable that the system does not know the identity of the consumer,the system cannot be 100% sure that the consumer is a grandparent. Forexample, even though a consumer's age is known to be over 60 years, thisconsumer may or may not be a grandparent. Using other information knownabout the consumer, e.g. transactions at toy stores near holidays,deposits into college savings investment vehicles, transactions atretailers selling young children's clothing, a weight, e.g. on a scaleof 1 to 10 or a percentage of trueness, can be assigned as to whetherthe consumer is to be classified in the “grandparent” classification.Even though the consumer is classified by the system as a grandparent,the consumer may not be a grandparent. That the consumer is not actuallya grandparent may not be that important to a marketer seeking to marketits goods and products to a grandparent. What can be important to amarketer is the fact that the consumer has the attributes of agrandparent.

Returning now to the trend identification phase 24, as previouslydiscussed, trend transactions can be useful in determiningclassifications such as grandparents and grandchildren's birthdays. Alsodetermined in the trend identification phase 24 are collateraltransactions which are useful for targeting advertisements. For example,a new car purchase may be an indicator useful for targetingadvertisements related to car care and car accessory equipment. It maybe useful to identify restaurant goers as potential movie attendees,particularly if they have a history of attending movies. Moreover,triggers can be set up to classify consumers based on life changingevents. For example, a movie goer who has recently had kids can now beclassified in a DVD renter/purchaser classification.

With reference now to FIG. 2, a block diagram is shown for matching thereceipt-level retail transactions 32 to the bank transactions 10. Aspreviously discussed, the account-level bank transaction 10 includesinformation related to the transaction, however, it is a summaryincluding the total transaction amount and does not include individualline item detail, e.g., exactly what goods or services were purchasedfrom the retailer or service provider. For example, the bank transactionshown includes a merchant identification 40, an authorization code 42, atotal dollar amount 44, an industry specific code 46, a zip code 48 ofwhere the transaction transpired, and a date/time stamp 50. It is to beunderstood that the bank transaction 10 shown in the figure is exemplaryonly for purposes of describing the present application. Data providedin the account-level bank transaction 10 can vary based on whatindividual financial institutions furnish to the campaign generationsystem. For example, the bank transaction shown in the figure includesan authorization code 42, however, some financial institutions may notprovide the authorization code. The receipt-level retail transactiondata 32 can also be stripped of personal information belonging to theconsumer, e.g. the credit card or account number of the consumer makingthe purchase. This is desirable to protect the anonymity of theconsumer.

Like with the account-level bank transaction 10, the receipt-levelretail transaction 32 is not limited to the receipt-level data 33 asshown in the figure. The receipt-level data 33, as shown, includes amerchant identification 52, an authorization code 54, a total dollaramount 56, a zip code of where the transaction transpired 58, adate/time stamp 60, and line item data 62. The line item data 62includes one or more line item entries 63 wherein each of the line itementries 63 corresponds to a product or service purchased. In theexemplary line item data entry 63 shown in the figure, there is includeda product identifier 64, a quantity 66, and a line item price 68. Theproblem now is how to match the receipt-level retail transactions 32 tothe corresponding account-level bank transactions 10 in theidentification/matching step 34.

With regard to matching the receipt-level retail transactions 32 to theaccount-level bank transactions 10, there are basically two scenariosdescribed herein. In the first scenario, the bank transaction 10 caninclude a merchant identifier 40 and an authorization code 42, and thereceipt-level retail transaction 32 similarly can include a merchantidentification 52 and a merchant authorization code 54. Because eachmerchant can uniquely generate an authorization code for each retailtransaction, the merchant identification 52 and the authorization code54 can be used to readily match the receipt-level retail transaction tothe account-level bank transaction simply by matching the respectivemerchant identifications 40, 52 and the respective authorization codes42, 54.

In the second scenario, unlike in the first scenario, the authorizationcode is unavailable in either or both of the account-level banktransaction 10 and the receipt-level retail transaction 32. In thissecond scenario, matching analytics are used in the identification step34 to match the receipt-level retail transactions 32 to theaccount-level bank transactions 10. Various analytical comparisons canbe utilized by the identification step 34. For example, if theaccount-level bank transactions 10 and the receipt-level retailtransactions 32 each include total dollar amount 44, 56, zip code of thetransaction 48, 58, and a date/time stamp 50, 60, the respectivetransactions can be matched with a sufficient degree of certainty basedon matching these three pieces of information. It is to be understood,however, that exact matches of individual fields are not required tosuccessfully identify matching transactions. For example, it is to beexpected that the date/time stamp provided with the receipt-level retailtransaction 32 will not exactly match the date/time stamp of theaccount-level bank transaction 10. One reason for this is that some timemay transpire between the time the retail establishment generated adate/time stamp for the receipt-level retail transaction and the timewhen the financial institution applies a date/time stamp to the receivedaccount-level bank transaction for the corresponding retail purchase.Although the date/time stamp may not exactly match, it is only necessarythat it be close enough or within a given range so that, when combinedwith the other data, such as the dollar amount and zip code, there is areasonable degree of certainty that the transactions can be successfullymatched.

It is also anticipated that range comparisons may be appropriate forother data fields used by the identification step 34. For example,although the total dollar amount 44, 56 is typically provided as anexact amount, e.g., $3.17, some financial institutions offer programs totheir customers which may cause the total dollar amount to vary. Forexample, at least one financial institution is known to offer a servicewhere the total dollar amount is rounded up to the nearest whole dollarwhen the retail transaction information is received by the financialinstitution, e.g., $3.17 is rounded up to $4.00. The difference betweenthe two amounts is then deposited into a savings account for theconsumer. In this particular case, the total dollar amount of thereceipt-level retail transaction 32 and the account-level banktransaction 10 can be expected to match only to the nearest dollar.However, if the particular financial institution offering this programalso provides account-level transaction data for the savings account towhich the difference is deposited, the identification step 34 canutilize additional analytics to identify the savings account deposit ofthe difference and thereby improve the accuracy of the identificationprocess. Again, it is to be appreciated that the identity of theconsumer is neither available nor needed in performing theseidentification analytics. Therefore, the consumer remains anonymous tothe system, thereby assuring privacy regarding the consumer'sidentification.

Various other issues with respect to matching the account-level banktransaction 10 to the associated receipt-level retail transactions 32are envisioned and fall within the scope of the present disclosure. Forexample, although the respective merchant identifications 40, 52, eachinclude a specific merchant identification code, thereby enabling anexact match of the merchant identifiers, it is to be understood that thesystem can also match the merchants based on character descriptions ofthe merchants. As long as a merchant text description is provided forthe merchant identification 40, 52, the account-level bank transactionsand the receipt-level retail transactions can be matched at the merchantidentification level.

Turning now to the line item entries 63, once the receipt-level retailtransaction 32 has been matched to the account-level bank transaction10, the line item entries can be further analyzed and utilized by thesystem. While the account-level bank transaction 10 is useful indetermining classifications for the consumer, it is not possible todetermine the particular products or services purchased based on theinformation provided, except in the rare instances when the merchantonly provides one particular product or one particular service. The lineitem entry 63 is advantageously used for this purpose. For example, theline item entry 63 includes at least a product identifier 64, a purchasequantity 66, and a line item price 68. These data items provide theinformation which enables the system to determine and track products andservices purchased by the consumer. This product information can beuseful in determining purchasing trends of the consumer and can also beuseful in anticipating future purchases based on the products purchased.For example, as previously described, a consumer purchasing drywallmight be expected to purchase paint in the near future.

A problem arises, however, from the fact that various retailestablishments utilize their own internal product identification codes.Therefore, additional processing of the product identifier 64 isnecessary to recode the product identifier to a common coding systemutilized by the campaign generation system. Identifying and convertingthe product identifier code to a common code can be accomplished in avariety of ways. One method, is to utilize a retailer database 70provided by the retailer. The retailer database 70 enables the system tolook up a product based on the product identifier 64 and obtainadditional information regarding the product from the database table. Asshown in the figure, the product database may include, associated withthe product identifier 72, an SKU number 74, and various descriptivedata for the product, such as, e.g., available colors 76. The databaseinformation, such as the SKU number 74, can be used to determine ataxonomy code for the product. For example, the retailer may utilize aninternal taxonomy 78 for code defining their products.

It is to be appreciated, however, that the internal taxonomy 78 may be aproprietary taxonomy or an internal taxonomy utilized only by theparticular retailer and may, therefore, not be directly useable by theadvertisement campaign generation system for identification purposes. Itis to be understood that, while the internal taxonomy 78 is shown as aseparate entity in the figure, wherein the taxonomy code can bedetermined from information in the product database 70, it is alsoanticipated that the retailer database 70 may directly incorporate ataxonomy code for the particular product identifier 72. It is alsoanticipated that the internal taxonomy 78 may also be the same taxonomyutilized by the advertising system and, therefore, can be used directlyfor product identification. In the event that an internal taxonomy 78 isutilized, taxonomy codes from the internal taxonomy can be readilymapped to a common taxonomy 80 utilized by the campaign generationsystem.

It is to be understood that while the figure shows only one retailer, anumber of retailers can be associated with the campaign generationsystem and operate in a similar fashion. For example, a second retailerinternal taxonomy 82 is shown in the figure and can be similarly mappedto the common taxonomy 80. It is also to be appreciated that someretailers may utilize a separate third party taxonomy. This, however,only makes it necessary to map the third party taxonomy codes to thecommon taxonomy 80. It is to be further appreciated that there will notalways exist an exact match between the internal taxonomy 78 and thecommon taxonomy 80. For example, some in-house or internal taxonomiescan be sparse in cases where, e.g., the retailer utilizes broad-basedtaxonomy codes which cover a broad range of products. The hierarchicalnature of the common taxonomy 80 can be advantageously utilized in thiscase. For example, the lowest levels in the common taxonomy 80 describeproducts to a high degree of detail. These lower level taxonomy codes,like the limbs of a tree, branch off of a higher level taxonomy codewhich represents a broader range. This enables the sparse internaltaxonomy to be mapped to the common taxonomy 80, however, the mappingsimply occurs at a higher level, i.e., broader level in the commontaxonomy 80.

It is again to be appreciated that the identification process 34described with reference to FIG. 2 is an anonymous matching processprotecting the identity of the consumer. Even so, some financialinstitutions may desire to reduce the risk of personal identificationeven further. For example, a financial institution may only be willingto provide total transaction amounts 44 rounded to the nearest $0.10.This poses little problem to the matching process 34 because theanalytics described provide for comparisons based on ranges aspreviously described. To further enhance security, it is envisioned thatthe advertising system can provide for the installation of exportsystems installed in-house in a site owned or operated by the financialinstitution desiring the service. Providing an in-house export systemenables the financial institution to implement filters which ensure thatno personal identification information is exported from the financialinstitution. The in-house export system may, e.g., be configured toexport only known, non-sensitive data columns. The in-house exportsystem can be further configured to perform an analytical securityscanning of the data being exported as an additional level of security.For example, the system may be configured to monitor the outgoing datato ensure that no data, such as personal names, account numbers, orsocial security numbers are being inadvertently exported. An in-houseexport system gives the financial institution an added degree ofconfidence that the identity of their customers is protected.

With reference now to FIG. 3, a process is shown in summary form foradvertising campaign creation. This process could also be employed fordelivering other targeted media to viewers, not only advertisements. Atargeting phase 84 utilizes, but is not limited to, three processingcomponents to develop a targeted list 86. The targeting components oftargeting phase 84 include transactional metrics as previously discussedbased on account-level bank transactions, e.g., how much a consumerspends on their car which can be categorized within selected ranges. Thepreviously described classifications 26 are useful for targetingadvertisements as also previously described. However, the targetingphase 84 also performs analytics on classifications which areconditional, i.e., conditional classifications. Conditionalclassifications are those classifications which may be added to thetargeted list or removed from the targeted list based on performance asdescribed further below.

The targeted list 86 includes consumers targeted for a particularadvertising campaign based on analytics performed by the campaigncreation phase 84. Advertisements 88 are delivered to consumer on thetargeted list 86 on web pages 90 as the advertising campaign progresses.Advertisements can be delivered over other platforms, e.g. filecommunication systems, that may not be conducted with the Internet. Theadvertisements 88 can also be delivered to consumers who are not foundon the targeted list. As the advertising campaign continues feedbackfrom the advertising phase 88 is analyzed, e.g., based on click-throughrates, and utilized to generate a performance classification chart 92.The performance classification chart 92 is used to identify the topperforming click-through advertisements. Based on these click-throughmetrics, a redistribution phase 94 can redistribute the campaign targetsin order to yield better results, i.e., higher click-through rates. Tothis end, embodiments of the present application are able to monitor thedelivered targeted advertisements and determine when a click-through hasoccurred. The campaign redistribution feature can be an optional part ofthe advertisement campaign process. Advertisers can choose to have anadvertisement campaign automatically analyzed and redistributed based onperformance or to have the advertisement campaign remain fixed, butreceive reports indicating not only the performance of the campaign, butpotential performance based on a redistribution of the campaign.

It is to be appreciated, therefore, that embodiments of the presentapplication not only effectively target consumers for advertisingcampaigns based on classifications, but also dynamically redistributethe campaign targets for improved results during the lifetime of theadvertising campaign. For example, one of the classifications 26 mightbe related to zip codes in the north east section of the United States.Therefore, based on zip code a classification can be created for thenorth east. During the advertising campaign, the performanceclassification 92 may indicate a much higher click-through rate for thenorth east classification as opposed to the remainder of the country.So, based on this, the advertising campaign can be redistributed orretargeted to show increasing advertisements to those people in thenorth east and fewer advertisements in other areas of the country. Itmay be possible, therefore, to target additional consumers in the northeast based on the north east classification which would have otherwisebeen missed in the original advertising campaign. Furthermore, if theadvertisements are being delivered to consumers not found on thetargeted list for the campaign, but the many consumers belonging to aparticular classification not part of the targeted list click on theadvertisement, then the advertisement can be redistributed to consumerswho fall within the classification that are clicking on theadvertisement.

It is to be appreciated that any data collected or mined in embodimentsof the present application, regardless of the source, can be used togenerate classifications. Retailer receipt data, or additional data fromthe retailer, browsing history, search history, and advertisement clickhistory, to name a few, can be used to generate classifications.Preferred embodiments are able to collate this information in order toprovide a more complete picture of the consumer. It is desirable,although not required, that the additional data still protect theanonymity of the consumer. The ADIC and the UCIC, being a unique butanonymous identification of the consumer, facilitates the collation ofthis consumer information.

With reference now to FIG. 4, a flow chart of an embodiment of thepresent application is shown. It is to be understood that each of thesteps in the flow chart corresponds to processing as describedpreviously with reference to FIG. 1. Therefore, the flow chart isdiscussed in a block summary form, without unnecessarily repeatingdetail previously described. It is also to be appreciated that the flowchart is provided for understanding embodiments of the presentapplication, however, the present application is not limited to thearrangement of steps shown in the figure. At step 100, an advertisingcampaign and delivery system receives bank consumer information fromfinancial institutions, the consumer information including consumeridentification and consumer related data. The consumer is identifiedanonymously by association with a unique customer identification code,UCIC, associated with the consumer. The received consumer information isfurther utilized by a classification maintenance and storage process 102which maintains classifications for use by an advertising campaigncreation procedure step 104.

The received consumer information is also used by a taxonomyclassification procedure 116 which determines taxonomy classificationcodes based on the received consumer information. The taxonomyclassification, however, is additionally based on account-level banktransaction data received in step 110 from financial institutions, theaccount-level bank transaction data including the anonymous consumeridentifier and merchant identifiers and transaction data related to thetransaction the consumer conducted with the merchant.

The received account-level bank transactions are further utilized by anaggregated transaction procedure 112 which identifies transactionsassociated with a particular consumer and aggregates them in order togenerate the previously described aggregate assumptions. At step 114,based on aggregation triggers, the aggregate assumptions are provided,i.e., bubbled up, in the form of classifications to the classificationmaintenance and storage process step 102. The taxonomy classificationsare further utilized by collateral and trend identification proceduresat step 122 to identify classifications based on collateraltransactions, timed transactions, and identified trends. Further, basedon triggers, key lifestyle information identified by the collateral andtrend identification procedure 122 are bubbled up at step 124 to theclassification maintenance and storage system step 102. As needed, theclassifications maintained and stored by the classification maintenanceand storage procedure 102 are provided to an advertising campaigncreation process step 104.

With reference now to FIG. 5, a flow chart is shown describing a methodof matching bank-level transactions to receipt-level transactions andidentifying line item products and services in the receipt-leveltransaction. Account-level bank transactions are received in step 130.The account-level bank transactions are received in a manner previouslydescribed with reference to FIGS. 1 and 2. It is to be understood thatthe system is configured to receive the account-level bank transactionsthrough a variety of processes and in a variety of formats. For example,some financial institutions, as previously described, may have in-houseexport systems installed to ensure that only non-sensitive data isprovided to the campaign generation. These in-house export systems canformat the data into a format suitable for the campaign generation. Onthe other hand, some financial institutions may desire, for the sake ofreduced processing overhead, to simply provide raw data to the campaigngeneration. In this event, the campaign generation is programmed to mapthe desired non-sensitive information to the requirements of thecampaign generation and protect the anonymity of the financialinstitution customers by not accepting any personally identifiableinformation. It is anticipated that a variety of filter andtransformation algorithms can be provided by the campaign generation inorder to accommodate a variety of financial institutions, each havingdifferent requirements.

Receipt-level retail transactions are received in step 132. Thereceipt-level transactions, similar to the account-level banktransactions, may be processed through various filters andtransformations to reformat the transactions from various formats usedby the various retailers into a common format utilized by the campaigngeneration system. It is also envisioned that retailer export systemsmay be installed in-house with the retailer for the purpose offormatting and/or ensuring the anonymity of the retail customers. If amerchant identification code and authorization code, as determined atstep 134, are available in each of the received account-level banktransactions and the receipt-level retailer transactions, thetransactions can be matched based on the provided merchantidentification code and the authorization code at step 136. Otherwise,as previously described with reference to FIG. 2, analytic processing atstep 138 can be utilized to match the transactions based on, e.g.,transaction dollar amount, zip code of the transaction, and a date/timestamp.

When the receipt-level retail transaction has been successfully matchedto an account-level bank transaction, the individual line item detailsof the receipt-level transaction can now be further processed at step140. In step 142, the individual line item product can be identified bymatching the product ID provided in the line item entry to a retailerdatabase utilizing the product ID as a search key. From data provided inthe retailer database, at step 144, the product ID can be translatedinto either one of a common taxonomy, an in-house retailer taxonomy, ora third party taxonomy. In the event that the product ID is matchedeither to an in-house retailer taxonomy or a third party taxonomy, theresulting taxonomy code can be further mapped at step 146 to the commontaxonomy code utilized by the advertising system.

With reference now to FIG. 6, a flow chart is provided describing theadvertising campaign creation procedure 104 as shown in FIG. 4. Aconsumer targeting step 150 targets consumers based on transactionalmetrics computed based on the received account-level transaction dataand, in some embodiments, also based on received receipt-level datareceived from participating merchants. Consumers are also targeted basedon the stored classifications and are further targeted based onconditional classifications as previously described. At step 152, atargeted consumer list is generated based on the consumers determined inthe consumer targeting step 150. These targeted consumers are identifiedanonymously by their associated ADIC or UCIC as previously described. Atstep 154, targeted advertisements are provided to the advertisementdelivery system for consumers in the targeted list. At step 156,advertisements provided by the advertisement delivery system aredisplayed as website content viewed by the targeted consumers. Contentother than advertisements can be displayed on devices that are connectedto a network other than the Internet. Also at step 156, advertisementscan be delivered to consumers, e.g. a random set of consumers, who arenot on the targeted list. At step 158, the advertisement delivery systemcollects data related to the number of advertisements displayed andassociated click-through data for performance evaluation. Thisperformance evaluation is fed back to the targeted list generation step152 which further provides this information to a classificationperformance ranking step 160. In this step, the click-throughperformance related to the stored classifications is analyzed and, atstep 162, the targeted list is redistributed based on deletion ofunderperforming classifications and addition of newly identifiedclassifications that show a high likelihood of a beneficialclick-through rate. For example these new classifications can beidentified from the consumers who were not on the targeted list, butreceived the target advertisement meant for the targeted list andclicked through the advertisement. If the ADIC is known for the randomcustomers who clicked on the targeted advertisement, then the ADIC canbe matched to the classifications in which the consumer is categorizedto find new consumers in classifications that were not originallytargeted by the advertisement.

Additionally, the system and method can allow an advertiser/marketer tocreate a broad advertising campaign and the system and method canevaluate the performance of the campaign in a similar manner to theclassification performance ranking step 160, above. The system willidentify the best performing classifications based on the click throughrate and then redistribute targeted advertisements to consumers in thebetter performing classifications.

The advertising campaign creation system can also be employed, withslight modifications, to restructure a web site, or other contentdisplayed on a networked device, based on classifications in which theviewer of the content falls into. For example, if the viewer is in aclassification indicating that he resides in Florida, then the weatherfor his region can be automatically delivered for display on the device.If recent transactions have taken place in another region of thecountry, then a website can deliver content, e.g. news and weather,based on that region.

The system is useful in delivering targeted content other thanadvertisements. Additional examples include delivering news articles,audio/visual and other content based on classifications andtransactional metrics that would interest the consumer when he visitsthe website. For example, if the viewer of espn.com falls into theclassification “golfer,” then espn.com could open with a golf article asopposed to its more generic article that opens for others who visit thesite. The system checks for the ADIC, which can be in the form of apersistent cookie, stored on the device that is loading and/or viewing awebsite, e.g. espn.com. In sum, the classifications and transactionalmetrics can be used to deliver content, other than advertisements, to adevice. Such a system and method for delivering advertisements andcontent can enhance a viewer's session, e.g. on the website, which canresult in the viewer wishing to return more often to view the content.The advertisements can be further personalized based on theclassifications and other information that is known about the consumer.This will be described in more detail below.

With reference now to FIG. 7, the interrelationship between those whowish to deliver targeted content and those who will receive the targetedcontent will be described. The system allows for communication among thefollowing individuals or entities: marketers 202, financial institutions204 and consumers 210. The financial institutions 204 can include banks,savings and loans, credit unions, and the like. As previously described,each of the financial institutions may include an in-house export system206 for exporting and/or filtering information and a secure database 218(or a plurality of secure databases) that is/are operated by thefinancial institutions 204 that stores, or warehouses, the consumerinformation and financial transactions (and other financial information)of the customers of the financial institution along with othernon-financial information. These financial transactions can include thedebits and credits of the customers of the bank, the loans that are heldby the bank for that customer, credit/debit card transactions and thelike. The other information about the consumer that is stored in thefinancial information secure database 218 includes the consumerinformation such as the identity of the consumer, the age and sex of theconsumer and the home zip code of the consumer. This consumerinformation is associated with a unique consumer identification code(UCIC) that associates the consumer to the information while stillmaintaining the anonymity of the consumer. By anonymity is meant thatthe information communicated to the advertising delivery providerprecludes the provider from knowing who the consumer really is so thatthe “cookie”, which can be later presented to the consumer, isanonymous. Accordingly, the UCIC can be referred to as an anonymouscoding. For example, the UCIC is not based on the name, address, e-mailaddress, phone number, a government issued identification such as asocial security number or the account number of the consumer at thefinancial institution, which could lead to the identity of the consumerbecoming known.

The UCIC is tied to the financial transactions of the consumer, the ageand sex of the consumer, and the zip code of the consumer; however, morepersonal information, such as the social security number, phone number,credit card numbers and the name of the consumer, is not associated withthe UCIC, thus protecting the identity of the consumer. The UCIC codesare communicated with the consumer information through an interface orportal, referred to as a non-consumer portal 220, to the processor 216.The advertisement delivery provider database 214 associates the UCICwith the information that is similar to that stored in the financialinstitutions databases. The advertisement delivery provider database 214stores, or warehouses, the financial information and other non-personalinformation that it receives from a number of different financialinstitutions. The advertisement delivery provider database 214 alsoassociates an advertisement delivery identification code (ADIC) and afinancial institution identification code (FIIDC) for each individualconsumer stored in its database and associates these codes with the UCICthat is provided by the financial institution database. The ADIC isunique to each consumer stored in the database. The ADIC can also beassociated with the device that is used to access content over thenetwork, which can allow a single ADIC to be associated with more thanone UCIC and FIIDC. Moreover, more than one ADIC can be associated withone UCIC. The FIIDC is associated with the financial institution thathas the provided the consumer information for the unique consumer. Sincethe UCIC maintains the anonymity of the consumer to which it is matched,the ADIC and the FIIDC also maintain that anonymity of the consumerbecause no personal information is matched to these codes. Accordingly,the UCIC and the ACIC can also be referred to as anonymous codings. TheADIC is assigned by the operator of the advertisement delivery providerdatabase 214 to the consumer 210 or to a specific device operated by theconsumer and utilized thereafter in communications between the consumer210 and the advertisement delivery provider for anonymous uniqueidentification of the consumer. The ADIC is further communicated to thecampaign generation system 216 so the system can correlate and combinedata for the consumer from multiple of the financial institutions 204.

The system can also allow companies or entities that are not financialinstitutions to allow for the delivery of advertisements on their websites or other communication platform—these entities will be referred toas third party advertisement presenters 212. The marketers 202 arecompanies or individuals who wish to deliver targeted advertisements totargeted consumers 210. The consumers 210 are also customers of at leastone of the financial institutions 204 that share information within thesystem. The consumer information, e.g., birth date, zip code, etc., andconsumer account-level transaction data, which is provided by thefinancial institutions, is used to determine classifications stored inan advertisement delivery provider database 214 maintained by a campaigngeneration system 216 operated by the advertisement delivery provider.The third party advertisement presenters 212 operate web sites or otherpublication outlets that are not affiliated with the any of thefinancial institutions (or are unsecure web sites that are operated bythe financial institutions) that allow for the delivery ofadvertisements. The system is designed to maintain the anonymity of theconsumers while allowing the marketers to have their advertisementsdelivered to consumers who fall within their defined market segment.Additionally, the system allows for communication to the third partyadvertisement presenters 212 (or other third party publishers ofcontent) to receive messages that instructs the presenter 212 to publishcontent based on at least one of the classifications associated with theconsumer viewing the webpage.

Generally the system incorporates an embodiment of the methods describedwith reference to FIGS. 4-6. The targeted advertisements based on theclassifications of the consumers 210 are delivered to the consumer whenthe consumer visits websites 222 (or other publication displays orapplications) having an association with the financial institutions 204or third party advertisement presenters 212. The targeted content, oneexample being advertisements, can be delivered either directly to theconsumer 210 or via the visited websites or other applications 222. Itis intended that the advertisement be able to be delivered to anydevice, e.g. computer, mobile phone, television set, that is able tostore a persistent cookie, or other persistent unique identifier.However, if an advertisement is being displayed on a secure applicationoperated by a financial institution, targeted content can be deliveredwithout having to set a persistent cookie.

With reference to FIG. 9, at 230 the content viewer, who is also acustomer of one of the financial institutions, logs onto the financialinstitution's website or securely enters an application that is operatedby or associated with the financial institution. The consumer logs ontoa protected portion of the financial institution's website orapplication where the consumer must identify himself appropriately sothat, for example, the financial institution allows the user to performbanking transactions over the Internet or other network. At 232, thefinancial institution passes the UCIC associated with the content viewerfrom the financial institution to the processor 216 (FIG. 7) operated bythe advertisement delivery provider, which has also been referred to asthe targeted content provider. At 234, the targeted content providerassociates and ADIC with the received UCIC. The advertisement deliveryprovider can check the advertisement delivery provider database 214(FIG. 7) for an ADIC associated with the received UCIC. If no ADIC isassociated with the received UCIC, then at 236 the advertisementdelivery provider can assign an ADIC to this device and set a cookie onthe device that includes the ADIC or other similar identification. Sincean ADIC has not been associated with this device, or this device haserased previous cookies including the ADIC, not enough information isknown about this device to send targeted content, e.g. an advertisementfor display on the device. Accordingly, at 238 a non-targetedadvertisement can be delivered at this time to the device.

With reference back to step 234, if an ADIC is associated with thereceived UCIC and the ADIC cookie is present on the device, then at 240the advertisement delivery provider checks to see if the ADIC cookiethat is present on the device matches the UCIC that is associated withthis ADIC. If the ADIC matches the UCIC, then enough information isknown about this device to send targeted content, because the financialinformation associated with the UCIC can be used to determine theclassifications into which the consumer using the device falls into.Accordingly, at 242 a targeted ad, or other targeted content, can bedelivered to the device.

With reference back to step 240, if the ADIC cookie stored on the devicedoes not match the UCIC associated with that ADIC, then at 246 theadvertisement delivery provider can record the association of the ADICstored on the device (via the persistent cookie that has been previouslyset) with another UCIC. This can happen for example, where the device isused to log onto two different financial institution applications. Whenlogging into the first financial institution application, theadvertisement delivery provider can assign an ADIC associated with theUCIC from the first financial institution. If this device is then usedto log onto another secure application of another financial institution,then an ADIC will have already been stored on the device, but the UCICfrom the new financial institution will not match the previouslyreceived ADIC. After recording the association of the ADIC with theanother (different) UCIC stored at 246, then at 248 the advertisementdelivery provider can query whether the another (different) UCIC and theADIC have been identified together enough to meet a threshold. If thisanother (different) UCIC and the stored ADIC have been identifiedtogether enough to meet a threshold, then enough information is knownabout the device to associate this another UCIC with the ADIC, thereforea single ADIC can now be associated with two UCICs and the financialinformation from two different financial institutions can be associatedwith one content viewer or device operated by a content viewer.Accordingly, enough information is known about the content viewer usingthis device that a targeted advertisement can be delivered. If the UCICand the ADIC have not been identified together enough to meet athreshold, then a non-targeted advertisement can be delivered.

Analytics can be used to determine whether the device that is being usedis a public computer. For example, if many different UCICs areassociated with the same ADIC, then an assumption can be made that thisdevice is being used by members of the general public and thereforegeneral advertisements can be delivered to this device. However, if thesame UCICs and the ADIC have been associated together many times, it islikely that this device is used by the same person who is a customer ofdifferent financial institutions. Accordingly, the financial informationavailable from these different institutions can be associated with thesame ADIC. The analytics used to determine whether the UCIC and the ADIChave been identified together enough to meet a threshold will depend onthe account type. For example, a consumer will likely not check thefinancial institution's application where the financial institutionholds a car loan for the consumer as often as the consumer will checkhis or her checking account. Accordingly, the UCIC associated with thecar loan account, may not have to be associated with the same ADIC asmuch as a checking account would in order to meet the thresholddescribed at step 248.

FIG. 9 depicts one method for delivering targeted content to a deviceover a network. FIG. 9 describes how the method includes identifying aconsumer who is viewing content based on an unique identifier stored onthe device used by the consumer to view the content. For the methoddescribed in FIG. 9, the unique identifier is a persistent cookie thatcan be set on the device used by the consumer to access the content.FIG. 11 depicts another method for delivering targeted content to adevice over a network and also employs an unique identifier associatedwith the device used by the consumer to view the content. FIG. 10describes a method for associating the unique identifier with thefinancial information of the consumer who uses the device.

With reference to FIG. 10, at 330 the content viewer, who is also acustomer of one of the financial institutions, logs onto a website orother application that is operated by the financial institution. Theconsumer logs onto a protected portion of the application where theconsumer must identify himself appropriately so that, for example, thefinancial institution allows the consumer to perform bankingtransactions over the network. At 332, the financial institution passesthe UCIC associated with the content viewer who has just logged into thesecured financial institution application to the targeted contentprovider. Another way of stating this is that the targeted contentprovided receives the UCIC. In this example, the UCIC is passed to theprocessor 216 (FIG. 7) operated by the advertisement delivery provider.At 334, the advertisement delivery provider captures the IP address forthe device used to log onto the secure financial institutionapplication. To protect the identity of the consumer who is using thedevice to view the secure portion of the financial institutionapplication, the IP address is hashed using a cryptographic hashfunction, e.g. MD5 and SHA-1. The IP address can only remain temporarilyin the server memory of the advertisement delivery provider, and then belet go from the memory of the advertisement delivery providers server.To further protect the identity of the user of the device, the IPaddress may not be written to a disk or stored in a database operated bythe advertisement delivery provider. The hash value for the IP address,also referred to herein as the hashed IP address, can be the onlyidentification associated with the device that is stored by theadvertisement delivery provider. Since it can be extremely difficult ornearly impossible to calculate a text, e.g. the IP address, that has agiven hash the IP address for the device used to access the secureapplication of the financial institution is not known to theadvertisement delivery provider. At 336, the hashed IP address is storedin a database operated by the advertisement delivery provider and at338, the association of the hashed IP address with the UCIC receivedfrom the financial institution is recorded. Accordingly, the device thatis used by the operator can be matched with the financial information ofthe operator, but the IP address of the device used by the operatorremains anonymous or unknown to the targeted content delivery providerdue to the hashing of the IP address. In addition to cryptographichashing, other encryption functions and algorithms can be applied to anIP address so that the IP addresses used to access financial institutionapplications are not stored on the server of the advertisement deliveryprovider.

FIG. 11 depicts a method for delivering targeted content to a deviceover a network using the hashed IP address of the device used to accessthe network. At 350, the consumer views a networked application, e.g. awebsite (the website need not be associated with a financialinstitution). At 352, the advertisement delivery provider captures theIP address for the device used to view the networked application. At354, the IP address is hashed, e.g. subjected to a cryptographic hashfunction, and let go from a server operated by the advertisementdelivery provider. Similar to what has been described above, in thisexample the IP address is not written to any database or disk under thecontrol of the advertisement delivery provider.

At 356, the advertisement delivery provider determines whether thehashed IP address is associated with a UCIC stored in its database. Ifthe hashed IP address is not associated with a UCIC in the database ofthe advertisement delivery provider, then at 358 not enough informationis known about the operator of the device to deliver targeted contentand therefore non-targeted content is delivered to the device. Ifhowever, the hashed IP address is associated with a UCIC at step 356then at 362, the advertisement delivery provider determines whether thehashed IP address has been associated with multiple UCICs. For example,for the device that is being used to access the networked applicationmay be a public computer and many different UCICs can be associated withthe same hashed IP address. Analytics can be used to determine whetherthe device that is being used to access the networked application is apublic computer. For example if many different UCICs are associated withthe same hashed IP address, then an assumption can be made that thisdevice is used by members of the general public.

At 362 if the hashed IP address has not been associated with multipleUCICs, then enough information is known about the operator of thedevice, since a single hashed IP address has been matched to a singleUCIC, and therefore the financial information associated with theconsumer that matches the UCIC can be associated with the hashed IPaddress. This allows for targeted content to be delivered to the deviceat 356. If, however, the hashed IP address has been associated withmultiple UCICs at 362, then at 366 the advertisement delivery providerdetermines whether the associations between this hashed IP address andthe multiple UCICs is typical. For example, if the same device is beingused to check a mortgage, a car loan, a checking account, and a savingsaccount, then all of these accounts may be owned by the same individualand it can be assumed that the individual who is operating the devicethat is checking these accounts is the owner of each of these accounts.Accordingly, the financial information associated with each of theseaccounts can be tied back to a unique identifier for the consumer, e.g.the hashed IP address of the device, and targeted content can then bedelivered to that device at step 364. However, if the associationsbetween this IP address and the multiple UCICs are not typical, forexample hundreds of different checking accounts have been associatedwith the same IP address, then this device associated with this IPaddress may be used by the general public and therefore the delivery oftargeted content based on financial information of one of the consumerswho operates the device would be difficult. Accordingly, non-targetedcontent can be delivered at step 358.

With reference to FIG. 8 an advertisement can be personalized to theconsumer by augmenting a base advertisement 300, which is based on theproduct or service that is being offered by the marketer or advertiser,with an augmentation 302 that is based on the classifications, the bankconsumer information, the account type information or the banktransaction information to deliver a blended advertisement 304. Anaugmentation of a base advertisement for a car will be provided as anexample. For example, if the consumer falls into the “golfer”classification and the advertisement that is to be delivered is for acar, then an image of the car that is being advertised can besuperimposed onto an image of a golf course. The blended advertisement304, which is this example is the car superimposed on a golf course, isthen delivered to the consumer's device, e.g. computer, mobile phone, orother device that is able to store a persistent cookie. Another mannerof stating the personalization of an advertisement is that a portion ofthe content of an advertisement is based on the classification or otherinformation that is known about the customer. The superimposition of oneimage, e.g. the car, over another image, e.g. a golf course, wouldresult in separate images appearing to the viewer of the advertisementas a single image, which is made up of the two images blended together.

The selection of the image that is based on a classification, i.e. theaugmentation 302, need not directly correlate to the classification inwhich the consumer is categorized. For example, the background color,which would be the augmentation, of the blended advertisement may bedetermined by a classification of the viewer of the advertisement. Forexample, a green background can be chosen for an advertisement of aproduct to persons who are in a classification “environmentalist” eventhough the product may not be associated with the environment, e.g. acomputer.

Moreover, the multiple classifications that the consumer viewing theadvertisement falls into can be weighted to determine the content of theadvertisement. For example, where the consumer is classified in both the“golfer” classification and the “grandparent” classification, thecontent of the augmentation, e.g. the background image of theadvertisement, can be a function of values given to differentclassifications. Values can be assigned to certain classifications sothat if the consumer falls into different classifications, then theclassification having the higher value is matched to the content for theadvertisement. In the example of a car advertisement, the “golfer”classification can be assigned a higher value than the “grandparent”classification so that an image associated with a golfer is displayedalong with the advertisement as opposed to an image associated with agrandparent.

The content of the advertisement can also include audio or video contentthat is based on the classification of the consumer viewing or listeningto the advertisement. For example, with reference back to a caradvertisement, audio content can be tailored so that consumers fallinginto the classification “college student” hear music popular on collegeradio stations and consumers falling into the classification “orchestraattendee” hear classical music, but the same car would be viewed foreach advertisement.

The content of the advertisement can also be a function of the retailtransaction data 33 (FIG. 2) for a specific consumer. For example, if itis known that a consumer recently purchased brand X coffee, then anadvertisement for brand Y coffee can be delivered to the consumer.Similarly, the retail transaction data can be helpful in deliveringtimely and geographically relevant advertisements. For example, if it isknown that a consumer recently paid for a dinner at a restaurant, thenadvertisements can be delivered to the consumer's mobile device fordesserts and other complementary products, e.g. coffee or movie tickets,for locations near the restaurant where the consumer dined. Thistechnology allows for these timely delivery of advertisements withoutthe need for global positioning devices to know the location of theconsumer—instead the receipt level data, which shows the location of therestaurant can provide the geographic information needed to provide thegeographically relevant advertisement.

The content of the advertisements delivered can also be morespecifically based on the account type information 13 (FIG. 1) for theindividual receiving the advertisement. For example, if it is known thatthe viewer of the advertisement has a car loan that is about to be paidoff, e.g. there are less than about three or four car payments remainingon an installment loan, then the advertisement that is delivered to theviewer can include information about the current loan and an offer for anew car. For example, the advertisement could read: “You have threepayments remaining for $356.75 per month for your 2004 Honda Accord, wecan get you into a 2009 Acura TL for $415 per month.”

The content of the advertisements can also include offers for productsand services from different marketers within the same advertisement. Forexample, the advertisement, “You have three payments remaining for$356.75 per month for your 2004 Honda Accord, we can get you into a 2009Acura TL for $415 per month,” could involve a car dealership and a bankpartnering together to offer the car and an installment loan.

The content of the advertisements delivered can also be based on banktransaction data 10 (FIG. 1). For example, if the bank transaction dataindicates that a consumer is paying $100 per month to natural gasprovider X, then an advertisement that can be delivered to the consumerthat includes information about this data. For example, an advertisementdelivered to the viewer could read: “You pay $100 per month for naturalgas to company X, switch to company Y and pay $80 per month.”

A method and system for generating targeted advertising campaigns basedon classifications has been described with reference to specificembodiments. Modifications and alterations will occur to those uponreading and understanding the preceding detailed description. Forexample, the method and system described above can also be used todeliver targeted content—content that is not an advertisement—toconsumers based on the above described classification and transactionalmetrics. The invention is not limited to only those embodimentsdescribed above. Instead, the invention is intended to cover allmodifications and alterations that come within the scope of the appendedclaims and the equivalents thereof.

1. A system for delivering targeted content to a device, the systemincluding: a database storing anonymous identifications for a pluralityof customers of at least one financial institution, each anonymousidentification being associated with financial information for arespective customer of the at least one financial institution; aprocessor including software configured to: classify the customers intoclassifications based on the financial information associated with eachanonymous identification; create a targeted list of content viewersbased on at least one selected classification; and deliver a targetedadvertisement over a network to a device used by a content viewer on thetargeted list after the device has been used to log onto an applicationoffered by the at least one financial institution, the targetadvertisement being presented on a website associated with the financialinstitution; and deliver a persistent cookie to the device after thedevice has been used to log into the application offered by the at leastone financial institution, wherein the persistent cookie is associatedwith at least one anonymous identification.
 2. The system of claim 1,wherein the software is further configured to: check for a uniqueidentifier stored on or associated with the device.
 3. The system ofclaim 2, wherein the software is further configured to: in response toreceiving confirmation that the unique identifier is stored on orassociated with the device, deliver at least one of (1) a targetedadvertisement to the device while the device is being used to viewanother application over the network or (2) a classification for thecontent viewer using the device to a content provider that is to displaycontent on the device, wherein the another application is not a secureapplication associated with the at least one financial institution andthe content provider is not associated with the at least one financialinstitution.
 4. The system of claim 2, wherein the unique identifier isat least one of a persistent cookie assigned by the system and an IPaddress for the device, and the anonymous identifications are receivedfrom the at least one financial institution.
 5. The system of claim 2,wherein the anonymous identifications and the unique identifiers are notassociated in the database with a name of the respective customer, abank account number for the respective customer, a street address forthe respective customer, an e-mail address for the respective customer,or a phone number for the respective customer.
 6. The system of claim 2,wherein the software is further configured to associate the uniqueidentifier stored on the device with at least two anonymousidentifications from at least two different financial institutions. 7.The system of claim 2, wherein the software is further configured toassociate at least two different unique identifiers with a respectiveanonymous identification.
 8. The system of claim 1, wherein the softwareis further configured to deliver a targeted advertisement that includesa first portion of content based on a product or service that is beingadvertised and a second portion of content based on at least one of theclassifications for the content viewer using the device.
 9. The systemof claim 1, wherein the financial information stored on the databaseincludes demographic information about the respective customer, retailtransaction data for the respective customer, bank transaction data forthe respective customer, and account type information for the respectivecustomer, and wherein the financial information is not associated with aname, a bank account number, a street address, an e-mail address, or aphone number for the respective customer.
 10. The system of claim 1,wherein the software is further configured to: aggregate the receivedfinancial information associated with at least one of the anonymousidentifications; determine at least one aggregate assumption based onthe aggregated financial information; determine at least one aggregateassumption classification based on the at least one aggregateassumption; and based on having determined at least one aggregateassumption classification, update the classification for the respectivecustomer associated with the at least one of the anonymousidentifications.
 11. The system of claim 1, wherein the software isfurther configured to: identify at least one customer trend comprising:determining at least one future collateral transaction based on thereceived financial information associated with at least one anonymousidentification; or determining at least one timed transaction based onthe received financial information associated with at least oneanonymous identification; and determine at least one customer trendclassification based on the at least one customer trend; and based onhaving determined at least one customer trend classification, update theclassification for the respective customer associated with the at leastone anonymous identification.
 12. The system of claim 1, wherein thesoftware is further configured to: record a number of clicks on thedelivered targeted advertisement; rank classifications associated withthe targeted advertisement based on the number of clicks recorded andthe classifications associated with the customers who have clicked onthe targeted advertisement; and identify underperforming classificationsassociated with the targeted advertisement based on a predetermineddesired click performance; identify new classifications to associatewith the targeted advertisement based on classifications associated withthe customers who have clicked on the targeted advertisement; and updatethe targeted list of content viewers based on the identifiedclassifications and the identified new classifications.
 13. A method fordelivering targeted content to a device over a network, the methodcomprising: receiving by a targeted content delivery system from atleast one financial institution customer financial information for aplurality of customers, wherein the customer financial information isassociated with a unique identification for each of the plurality ofcustomers; generating classifications for the plurality of customersbased on the received customer financial information; selecting at leastone of the classifications for targeted content; creating a list oftargeted customers selected from the plurality of customers based on theselected classifications; and delivering the targeted content over anetwork to respective devices connected with the network and operated bythe targeted customers on the created list, wherein the targeted contentis delivered to each respective device, on a webpage associated with thefinancial institution, after each respective device has been used to logonto an application offered by the at least one financial institution.14. The method of claim 13, wherein the customer financial informationincludes transactions at a plurality of retailers or service providers.15. The method of claim 13, wherein the customer financial informationis associated with a first anonymous identification assigned by therespective financial institution and a second anonymous identificationassigned by the targeted content delivery system.
 16. The method ofclaim 15, further comprising: identifying a customer who is viewingcontent based on a unique identifier stored on or associated with thedevice used by the customer to view the content, wherein the uniqueidentifier stored on the device is associated with the second anonymousidentification; matching the customer to the targeted content based onmatching the identified customer to the list of targeted customers; anddelivering the targeted content to the customer for display on thedevice used by the customer.
 17. The method of claim 15, wherein eachunique identification for each of the plurality of customers isanonymous and lacks a name of the customer, a bank account number forthe customer, a street address for the customer, an e-mail address forthe customer, or a phone number for the customer.
 18. The method ofclaim 17, wherein identifying a customer includes checking for theunique identifier stored on the device and matching any second anonymousidentifications associated with the unique identifier with respectivefirst anonymous identifications assigned by the financial institutions.19. The method of claim 18, wherein the unique identifier stored on thedevice is a persistent cookie stored on the device or based on an IPaddress associated with the device.
 20. The method of claim 13, furthercomprising: aggregating the received financial transaction informationfor at least one customer; determining at least one aggregate assumptionbased on the aggregated financial transaction information; determiningat least one aggregate assumption classification based on the at leastone aggregate assumption; and based on having determined at least oneaggregate assumption classification for at least one of the customers,updating the generated classifications for the at least one of thecustomers.
 21. The method of claim 13, further comprising: determiningat least one taxonomy classification code for each of he customers basedon the received customer financial information; determining at least onetaxonomy classification based on the at least one taxonomyclassification code; and based on having determined at least onetaxonomy classification for at least one of the customers, updating thegenerated classifications for the at least one of the customers.
 22. Themethod of claim 13, further comprising: identifying at least onecustomer trend for at least one of the customers, the identifying atleast one customer trend comprising: determining at least one futurecollateral transaction based on the received customer financialinformation; or determining at least one timed transaction based on thereceived customer financial information; determining at least onecustomer trend classification based on the at least one customer trend;and based on having determined at least one customer trendclassification for at least one of the customers, updating the generatedclassifications for the at least one of the customers.
 23. The method ofclaim 13, further comprising: checking for a unique identifier on orassociated with the device used by a customer to view the targetedcontent, recording a number of clicks on the delivered targeted contentand the unique identifier associated with each of the clicks; rankingclassifications associated with the targeted content based on the numberof clicks recorded and the classifications associated with the recordedunique identifier; and identifying underperforming classificationsassociated with the targeted content based on a predetermined desiredclick performance; identifying new classifications to associate with thetargeted content based on classifications associated with the recordedunique identifier; and updating the list of targeted customers based onthe identified underperforming classifications and the identified newclassifications.
 24. The method of claim 13, wherein the targetedcontent includes a first portion of content based on the classificationgenerated for the customer receiving the targeted content and a secondportion of content is generic to all viewers of the targeted content.25. The method of claim 13, wherein generating classifications for theplurality of customers based on the received customer financialinformation, each classification represents an analytically deducedclassification which includes weighing whether a customer is to beclassified in a classification based on a percentage of trueness.